Add accuracy curve (accgraph) strategy for BeatLeader that filters by a star range
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@ -21,10 +21,25 @@ from helpers.SimpleBeatLeaderAPI import SimpleBeatLeaderAPI
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from helpers.SimpleBeatSaverAPI import SimpleBeatSaverAPI
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from saberlist.utils import reset_history
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from saberlist.playlist_strategies.oldscores import playlist_strategy_beatleader_oldscores, playlist_strategy_scoresaber_oldscores
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from saberlist.playlist_strategies.accuracy import playlist_strategy_beatleader_lowest_acc, playlist_strategy_beatleader_accuracy_gaps, playlist_strategy_scoresaber_accuracy_gaps
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from saberlist.playlist_strategies.performance import playlist_strategy_beatleader_lowest_pp, playlist_strategy_scoresaber_lowest_pp
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from saberlist.playlist_strategies.beatsaver import playlist_strategy_beatsaver_acc, playlist_strategy_beatsaver_curated, playlist_strategy_beatsaver_mappers
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from saberlist.playlist_strategies.oldscores import (
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playlist_strategy_beatleader_oldscores,
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playlist_strategy_scoresaber_oldscores,
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)
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from saberlist.playlist_strategies.accuracy import (
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playlist_strategy_beatleader_lowest_acc,
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playlist_strategy_beatleader_accuracy_gaps,
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playlist_strategy_scoresaber_accuracy_gaps,
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playlist_strategy_beatleader_accuracy_gaps_star_range,
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)
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from saberlist.playlist_strategies.performance import (
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playlist_strategy_beatleader_lowest_pp,
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playlist_strategy_scoresaber_lowest_pp,
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)
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from saberlist.playlist_strategies.beatsaver import (
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playlist_strategy_beatsaver_acc,
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playlist_strategy_beatsaver_curated,
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playlist_strategy_beatsaver_mappers,
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)
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def saberlist() -> None:
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"""
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@ -44,7 +59,7 @@ def saberlist() -> None:
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playlist_builder = PlaylistBuilder(covers_dir='./covers/beatsavers')
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elif strategy == 'beatleader_lowest_pp':
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playlist_data, playlist_title = playlist_strategy_beatleader_lowest_pp(BeatLeaderAPI(cache_expiry_days=CACHE_EXPIRY_DAYS))
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playlist_builder = PlaylistBuilder(covers_dir='./covers/beatleader')
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playlist_builder = PlaylistBuilder(covers_dir='./covers/pajamas')
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elif strategy == 'scoresaber_lowest_pp':
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playlist_data, playlist_title = playlist_strategy_scoresaber_lowest_pp(ScoreSaberAPI(cache_expiry_days=CACHE_EXPIRY_DAYS))
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playlist_builder = PlaylistBuilder(covers_dir='./covers/scoresaber')
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@ -54,6 +69,10 @@ def saberlist() -> None:
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elif strategy == 'beatleader_accuracy_gaps':
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playlist_data, playlist_title = playlist_strategy_beatleader_accuracy_gaps(SimpleBeatLeaderAPI(cache_expiry_days=CACHE_EXPIRY_DAYS))
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playlist_builder = PlaylistBuilder(covers_dir='./covers/pajamas')
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elif strategy == 'beatleader_accuracy_gaps_star_range':
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input_star_level = input("Enter star level (Default: 6)") or 6
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playlist_data, playlist_title = playlist_strategy_beatleader_accuracy_gaps_star_range(SimpleBeatLeaderAPI(cache_expiry_days=CACHE_EXPIRY_DAYS), star_level=float(input_star_level))
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playlist_builder = PlaylistBuilder(covers_dir='./covers/pajamas')
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elif strategy == 'scoresaber_accuracy_gaps':
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playlist_data, playlist_title = playlist_strategy_scoresaber_accuracy_gaps(ScoreSaberAPI(cache_expiry_days=CACHE_EXPIRY_DAYS))
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playlist_builder = PlaylistBuilder(covers_dir='./covers/scoresaber')
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@ -63,11 +82,14 @@ def saberlist() -> None:
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elif strategy == 'beatsaver_mappers':
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playlist_data, playlist_title = playlist_strategy_beatsaver_mappers(SimpleBeatSaverAPI())
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playlist_builder = PlaylistBuilder(covers_dir='./covers/pajamas')
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elif strategy == 'blank_playlist':
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playlist_data, playlist_title = [], input("Enter playlist title: ")
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playlist_builder = PlaylistBuilder(covers_dir='./covers/pajamas')
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else:
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logging.error(f"Unknown strategy '{strategy}'")
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return
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if not playlist_data:
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if not playlist_data and strategy != 'blank_playlist':
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logging.info("No new scores found to add to the playlist.")
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return
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@ -88,9 +110,11 @@ def get_strategy():
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# "scoresaber_lowest_pp",
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# "beatleader_lowest_acc",
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"beatleader_accuracy_gaps",
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"beatleader_accuracy_gaps_star_range",
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"scoresaber_accuracy_gaps",
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"beatsaver_curated",
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"beatsaver_mappers"
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"beatsaver_mappers",
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"blank_playlist"
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],
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help="Specify the playlist generation strategy")
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parser.add_argument("-r", "--reset",
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@ -391,3 +391,157 @@ def playlist_strategy_beatleader_lowest_acc(
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save_history(history)
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return playlist_data, f"beatleader_lowest_acc-{new_count:02d}"
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def playlist_strategy_beatleader_accuracy_gaps_star_range(
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api: SimpleBeatLeaderAPI,
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star_level: float,
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song_count: int = 40,
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bin_size: float = 0.25,
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bin_sort: bool = False
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) -> List[Dict[str, Any]]:
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"""
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Build a playlist of songs where the player's accuracy is furthest below the median accuracy
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for a specified star rating range. Songs are grouped into bins by star rating within the
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specified range to ensure fair comparison.
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Like beatleader_accuracy_gaps, but only for a specific star rating range.
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:param api: SimpleBeatLeaderAPI instance for making API calls
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:param star_level: Specific star level to filter songs (e.g., 6.0 for songs ranging from 6.00 to 6.99)
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:param song_count: Number of songs to include in the playlist
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:param bin_size: Size of star rating bins for grouping similar difficulty songs
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:param bin_sort: Whether to sort the bins by star rating
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:return: A tuple containing (list of song dictionaries, playlist title string)
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"""
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player_id = prompt_for_player_id()
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history = load_history()
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history.setdefault('beatleader_accuracy_gaps_star_range', {})
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history.setdefault('playlist_counts', {})
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# Validate star_level
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if not isinstance(star_level, (int, float)) or star_level < 0:
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logging.error("Invalid star_level provided. It must be a non-negative number.")
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return [], ""
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# Define star range based on the specified star_level
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star_min = star_level
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star_max = star_level + 0.99 # Inclusive of the specified star_level up to the next whole number minus one cent
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# Get the current count and increment it
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count_key = f'beatleader_accuracy_gaps_star_{star_level}'
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current_count = history['playlist_counts'].get(count_key, 0)
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new_count = current_count + 1
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history['playlist_counts'][count_key] = new_count
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# Fetch accuracy graph data
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all_scores = api.get_player_accgraph(player_id)
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if not all_scores:
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logging.warning(f"No accgraph data found for player ID {player_id} on BeatLeader.")
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return [], ""
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logging.debug(f"Found {len(all_scores)} accgraph entries for player ID {player_id} on BeatLeader.")
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# Filter scores within the specified star range
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filtered_scores = [
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entry for entry in all_scores
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if entry.get('stars') is not None and star_min <= entry['stars'] <= star_max
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]
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if not filtered_scores:
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logging.warning(f"No accgraph entries found within star range {star_min} to {star_max}.")
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return [], ""
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logging.debug(f"Found {len(filtered_scores)} accgraph entries within star range {star_min} to {star_max}.")
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# Collect all star ratings within the specified range
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star_ratings = [entry['stars'] for entry in filtered_scores if entry.get('stars') is not None]
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min_stars = min(star_ratings)
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max_stars = max(star_ratings)
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star_range = max_stars - min_stars
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# Determine number of bins
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num_bins = math.ceil(star_range / bin_size)
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logging.info(f"Using bin size: {bin_size}, resulting in {num_bins} bins within star range {star_min} to {star_max}.")
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# Group accuracies by bins
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bin_to_accuracies = defaultdict(list)
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for entry in filtered_scores:
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stars = entry.get('stars')
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acc = entry.get('acc')
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if stars is None or acc is None:
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continue
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bin_index = int((stars - min_stars) / bin_size)
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bin_to_accuracies[bin_index].append(acc)
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# Calculate median accuracy for each bin
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bin_to_median = {}
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for bin_index, accs in bin_to_accuracies.items():
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bin_to_median[bin_index] = median(accs)
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bin_start = min_stars + bin_index * bin_size
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bin_end = bin_start + bin_size
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logging.debug(f"Median accuracy for bin {bin_index} (stars {bin_start:.2f} to {bin_end:.2f}): {bin_to_median[bin_index]:.4f}")
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# Compute difference from median for each score
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for entry in filtered_scores:
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stars = entry.get('stars')
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acc = entry.get('acc')
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if stars is not None and acc is not None:
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bin_index = int((stars - min_stars) / bin_size)
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median_acc = bin_to_median.get(bin_index)
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if median_acc is not None:
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entry['diff_from_median'] = acc - median_acc
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else:
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entry['diff_from_median'] = float('inf') # Place entries with missing data at the end
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else:
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entry['diff_from_median'] = float('inf') # Place entries with missing data at the end
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# Sort scores by difference from median (ascending: most below median first)
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filtered_scores.sort(key=lambda x: x.get('diff_from_median', float('inf')))
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playlist_data = []
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for score_entry in filtered_scores:
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if len(playlist_data) >= song_count:
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break
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acc = score_entry.get('acc', 0)
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stars = score_entry.get('stars')
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song_hash = score_entry.get('hash')
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if not song_hash or stars is None:
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logging.debug(f"Skipping entry due to missing hash or stars: {score_entry}")
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continue
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difficulty = score_entry.get('diff', '')
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difficulty_characteristic = score_entry.get('mode', 'Standard')
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if song_hash in history['beatleader_accuracy_gaps_star_range'] and difficulty in history['beatleader_accuracy_gaps_star_range'][song_hash]:
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logging.debug(f"Skipping song {song_hash} with difficulty {difficulty} as it's in history.")
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continue
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song_dict = {
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'hash': song_hash,
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'difficulties': [
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{
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'name': difficulty,
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'characteristic': difficulty_characteristic
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}
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]
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}
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playlist_data.append(song_dict)
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logging.debug(f"Selected song for playlist: Hash={song_hash}, Difficulty={difficulty}, "
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f"Accuracy={acc*100:.2f}%, Diff from Median={score_entry['diff_from_median']*100:.2f}%")
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# Update history
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history['beatleader_accuracy_gaps_star_range'].setdefault(song_hash, []).append(difficulty)
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if not playlist_data:
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logging.info(f"No new songs found to add to the playlist based on history for BeatLeader accuracy gaps within star range {star_min} to {star_max}.")
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else:
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for song in playlist_data:
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song_hash = song['hash']
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difficulty = song['difficulties'][0]['name']
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logging.info(f"Song added: Hash={song_hash}, Difficulty={difficulty}")
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logging.info(f"Total songs added to playlist from BeatLeader accuracy gaps within star range {star_min} to {star_max}: {len(playlist_data)}")
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save_history(history)
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playlist_title = f"beatleader_accgraph_star_{star_level}-{new_count:02d}"
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return playlist_data, playlist_title
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